RNAmp RNA输出分析综述。(A)当癌细胞的RNA输出高于正常细胞水平时,就会发生超转录(左)。从原发肿瘤组织中提取RNA时,每个细胞信息的RNA输出量丢失(中)。癌细胞和正常细胞特异性转录本可以使用肿瘤特异性标记物变体进行识别,如体细胞取代(Subs)和LOH-SNPs(右)。(B) DNA和RNA VAF在有和没有超转录(HyperTX)的样品中的分布。肿瘤特异性变异的RNA VAF阳性转移表明RNA输出增加。为了估计癌症细胞相对于正常细胞的RNA输出的总体折叠变化,RNAmp将这些VAF移位与肿瘤纯度、倍性和局部拷贝数数据结合起来。(C)对肿瘤细胞和正常细胞混合物进行细胞数归一化RNA-seq,以验证RNAmp的准确性。在细胞混合前测量每个细胞的RNA输出量。然后用RNAmp对这些混合物进行测序和处理。 (D) Fold change in RNA output levels of cancer cell lines measured by direct RNA quantification. Error bars correspond to SD. (E) RNAmp-derived RNA output measures (boxplots) compared to direct RNA quantification measures (red diamonds). Boxplot center line corresponds to the median, box limits are upper and lower quartiles, and whiskers represent 1.5 × interquartile range. (F) Pearson correlation of RNAmp-derived tumor RNA content estimates compared to direct RNA content quantification (R = 0.99, P < 0.0001). (G) RNA output per cell measured in medulloblastoma cells with and without MYC induction. (H) RNAmp-derived fold change in RNA output between UW228 Myc and UW228 wild-type cells (boxplot) compared to direct RNA quantification (red line). Boxplots are defined in (E). Credit:科学的进步(2022)。DOI: 10.1126 / sciadv.abn0238